Apparatus, method, and program for assisting creation of contents to be used in interventions, and computer-readable recording medium

ABSTRACT

A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.

This application is a Continuation Application of PCT InternationalApplication No. PCT/JP2021/016812, filed on Apr. 27, 2021, and the PCTInternational Application is based upon and claims the benefit ofpriority from Japanese Patent Application No. 2020-089377, filed on May22, 2020, the entire contents of which are incorporated herein byreference.

TECHNICAL FIELD

The present invention relates to an apparatus, a method, and a programthat assist creation of contents to be used in interventions, andrelates to a computer-readable recording medium that records theprogram.

BACKGROUND

A technology for individually predicting effects of interventions (suchas advertisements and medical practice) to intervention targets (such asusers of the WEB to whom the advertisements are individually displayedand patients to whom the medical treatment such as surgery isindividually applied) has been known. For example, Japanese PatentApplication Laid-open No. 2015-53071 discloses an invention that allowsmeasured causal effects to be utilized in information transmission.

SUMMARY

Incidentally, in the field of WEB advertising, contents such as bannersto be presented to the users may have significant influence on purchaserates of commercial products. Thus, it is important to create contentsthat can increase the purchase rates. However, hitherto, the contentshave been created without clear guidelines on which factor of thecontents has influence on the purchase rates. Thus, a proportion inwhich the creation depends on senses of creators of the contents hasbeen high, and hence there has been a disadvantage that creation ofcontents having great advertising effects is difficult.

The present invention has been made in view of such circumstances, andit is an object thereof to provide an apparatus, a method, and a programthat are capable of facilitating creation of contents having greaterintervention effects, and to provide a computer-readable recordingmedium that records the program.

According to a first aspect of the present invention, there is providedan apparatus configured to assist creation of contents to be used ininterventions, the apparatus including:

a processing unit including at least one processor; and

a storage unit configured to store a command to be executed by theprocessing unit,

in which the interventions include presentation of the contents to aplurality of targets so that the plurality of targets provoke reactionsthat a beneficiary wants,

in which, among respective predicted-intervention effects on theplurality of targets, one predicted-intervention effect on one targetamong the plurality of targets is a numerical value corresponding to anincrease in gain to the beneficiary, the gain being expected to belarger in a case where a corresponding one intervention among theinterventions is implemented to the one target than in a case where thecorresponding one intervention is unimplemented to the one target,

in which the gain is a numerical value that is set in accordance with aresult of a corresponding one reaction by the one target among thereactions,

in which the processing unit executes, in accordance with the command,

-   -   a process of calculating contribution degrees that indicate        respective degrees by which a plurality of attribute items        included as predetermined attributes of the one target        contribute to the one predicted-intervention effect, the        calculating being performed on a basis of an estimation model        for estimating the one predicted-intervention effect from the        predetermined attributes of the one target,    -   a process of generating a display screen that can be displayed        on a display to be used in work of creating the contents, and

in which the process of generating the display screen includesgenerating the display screen on which at least one contribution degreeamong the contribution degrees calculated respectively with regard tothe plurality of attribute items is displayed.

According to a second aspect of the present invention, there is provideda method of assisting creation of contents to be used in interventions,

the interventions including presentation of the contents to a pluralityof targets so that the plurality of targets provoke reactions that abeneficiary wants,

among respective predicted-intervention effects on the plurality oftargets, one predicted-intervention effect on one target among theplurality of targets being a numerical value corresponding to anincrease in gain to the beneficiary, the gain being expected to belarger in a case where a corresponding one intervention among theinterventions is implemented to the one target than in a case where thecorresponding one intervention is unimplemented to the one target,

the gain being a numerical value that is set in accordance with a resultof a corresponding one reaction by the one target among the reactions,

the method including:

-   -   calculating, by at least one computer, contribution degrees that        indicate respective degrees by which a plurality of attribute        items included as predetermined attributes of the one target        contribute to the one predicted-intervention effect, the        calculating being performed on a basis of an estimation model        for estimating the one predicted-intervention effect from the        predetermined attributes of the one target; and    -   generating, by the at least one computer, a display screen that        can be displayed on a display to be used in work of creating the        contents,

in which the generating of the display screen by the at least onecomputer includes generating the display screen on which at least onecontribution degree among the contribution degrees calculatedrespectively with regard to the plurality of attribute items isdisplayed.

According to a third aspect of the present invention, there is provideda program which assists creation of contents to be used ininterventions,

the interventions including presentation of the contents to a pluralityof targets so that the plurality of targets provoke reactions that abeneficiary wants,

among respective predicted-intervention effects on the plurality oftargets, one predicted-intervention effect on one target among theplurality of targets being a numerical value corresponding to anincrease in gain to the beneficiary, the gain being expected to belarger in a case where a corresponding one intervention among theinterventions is implemented to the one target than in a case where thecorresponding one intervention is unimplemented to the one target,

the gain being a numerical value that is set in accordance with a resultof a corresponding one reaction by the one target among the reactions,

the program causing at least one computer to execute:

-   -   a process of calculating contribution degrees that indicate        respective degrees by which a plurality of attribute items        included as predetermined attributes of the one target        contribute to the one predicted-intervention effect, the        calculating being performed on a basis of an estimation model        for estimating the one predicted-intervention effect from the        predetermined attributes of the one target; and    -   a process of generating a display screen that can be displayed        on a display to be used in work of creating the contents,

in which the process of generating the display screen includesgenerating the display screen on which at least one contribution degreeamong the contribution degrees calculated respectively with regard tothe plurality of attribute items is displayed.

According to a fourth aspect of the present invention, there is provideda computer-readable recording medium that records the program accordingto the third aspect.

According to the present invention, it is possible to provide anapparatus, a method, and a program that are capable of facilitatingcreation of contents having greater intervention effects, and to providea computer-readable recording medium that records the program.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing an example of a configuration of a systemincluding a server apparatus according to an embodiment of the presentinvention.

FIG. 2 is a diagram showing an example of a configuration of the serverapparatus according to the embodiment of the present invention.

FIG. 3A is a table showing an example of target information, FIG. 3B isa table showing an example of intervention information, and FIG. 3C is atable showing an example of content information.

FIG. 4A is a table showing an example of result information, FIG. 4B isa table showing an example of intervened-group result information, andFIG. 4C is a table showing an example of non-intervened-group resultinformation.

FIG. 5 is a table showing an example of target attribute information.

FIG. 6A is a table showing the intervened-group result information andthe target attribute information to be used in generating a first-gainestimation model, and FIG. 6B is a table showing thenon-intervened-group result information and the target attributeinformation to be used in generating a second-gain estimation model.

FIG. 7 is a table showing an example of predicted intervention effectscalculated respectively with regard to targets.

FIG. 8 is a table showing an example of respective weightingcoefficients of attribute items in a linear estimation model.

FIG. 9 is a chart showing an example of results obtained by analyzing atendency of the predicted intervention effects corresponding to theattribute items of the targets with a decision tree.

FIG. 10 is an explanatory flowchart showing an example of operations inthe server apparatus according to the embodiment of the presentinvention.

FIG. 11 is a view showing an example of a display screen for work ofcreating contents.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 is a diagram showing an example of a configuration of a systemincluding a server apparatus 1 according to an embodiment of the presentinvention. The system shown in FIG. 1 includes the server apparatus 1and an information processing apparatus 2 that are communicable witheach other via a network 9 such as the Internet. The server apparatus 1is an apparatus to be managed by an entity that implements interventions(below, sometimes referred to as an “intervener”). The informationprocessing apparatus 2 is a terminal apparatus to be used in work ofcreating contents for the interventions (such as banners foradvertising). Herein, the “intervention” means an action to be taken fortargets so that the targets provoke predetermined reactions that abeneficiary wants. In addition, the “beneficiary” means an entity thatreceives a resultant profit from the intervention. In the followingdescription, as an example, the “intervention” is defined as advertisingon a WEB site, the “target” is defined as a user on the Internet whovisits the WEB site, the “beneficiary” is defined as an advertiser whorequests the advertising on the WEB site, and the “intervener” isdefined as a service provider involved in a service of the advertisingon the WEB site in response to the request from the advertiser. Notethat, the advertising on the WEB site is merely an example of the“intervention,” and the present invention is not limited to thisexample. In other words, concepts of the “intervention,” the“intervener,” the “beneficiary,” and the like of the present inventionare applicable also to various other fields.

FIG. 2 is a diagram showing an example of a configuration of the serverapparatus 1 according to this embodiment. The server apparatus 1 shownin FIG. 1 includes a communication interface 10, an input/output device20, a storage unit 30, and a processing unit 40.

The communication interface 10 is an apparatus for communicating withother apparatuses (such as the information processing apparatus 2) viathe network 9, and includes a communication device such as a networkinterface card that performs the communication according to apredetermined communication standard such as Ethernet (trademark) or awireless LAN.

The input/output device 20 has at least one of an input function toinput instructions in response to operations by a user and otherinformation to the processing unit 40, and an output function to outputinformation from the processing unit 40. For example, the input/outputdevice 20 includes at least one of a device having the input function,such as a keyboard, a mouse, a touchpad, a microphone, or a camera, adevice having the output function, such as a display or a speaker, and adevice having an input/output function, such as a touchscreen.

The storage unit 30 stores a program including a command to be executedby the processing unit 40, and stores, for example, data to betemporarily stored during processes to be executed by the processingunit 40, data to be used in the processes to be executed by theprocessing unit 40, and resultant data from the processes executed bythe processing unit 40. More specifically, the storage unit 30 stores,for example, target information 31 (FIG. 3A), intervention information32 (FIG. 3B), and content information 33 (FIG. 3C) described below.

The program to be stored in the storage unit 30 may be, for example,read out of a storage apparatus (such as a USB memory) connected to aninterface such as an USB of the input/output device 20, may be read outof a computer-readable recording medium (a non-transitory tangiblerecording medium such as an optical disk) with a recording-mediumreading apparatus of the input/output device 20, or may be downloadedvia the communication interface 10 from another apparatus connected tothe network 9.

The storage unit 30 includes main storage apparatuses (such as a ROM anda RAM), and auxiliary storage apparatuses (such as a flash memory, anSSD, a hard disk, and an optical disk). The storage unit 30 may beconstituted by one among the plurality of these storage apparatuses, ormay be constituted by the plurality of these storage apparatuses. Thesestorage apparatuses constituting the storage unit 30 are connected tothe processing unit 40 via a bus of a computer or other communicationmeans.

The processing unit 40 collectively controls overall operations in theserver apparatus 1, and executes predetermined information processes.The processing unit 40 includes one or more processors (such as a CPU oran MPU) that execute, for example, processes in accordance with thecommands of the one or more programs stored in the storage unit 30. Whenthe one or more processors execute the commands of the one or moreprograms stored in the storage unit 30, the processing unit 40 operatesas one or more computers.

The processing unit 40 may include one or more dedicated hardwaremodules (such as an ASIC and an FPGA) configured to implement specificfunctions. In this case, the processing unit 40 may execute, as the oneor more computers, processes described below that relate to assistancein creating the contents to be used in the interventions, or thededicated hardware modules may execute at least some of these processes.

As shown, for example, in FIG. 2 , as components that execute processesrelating to the assistance in creating the contents to be used in theinterventions, the processing unit 40 includes an interventionimplementation unit 41, an estimation-model generation unit 42, acontribution-degree calculation unit 43, and a screen generation unit44.

The intervention implementation unit 41 executes a process forimplementing the interventions with respect to the targets to beintervention targets. If the advertising on the WEB site is implementedas the “interventions,” for example, the intervention implementationunit 41 may execute a process as a DSP (Demand Side Platform) that winsa bid for advertising spaces on the WEB site in response to the requestfrom the advertiser, and that distributes advertisements to the WEBsite.

The estimation-model generation unit 42 executes a process of generatingan estimation model for estimating predicted intervention effects frompredetermined attributes of the targets. Among the predictedintervention effects, one predicted-intervention effect on a certain oneof the targets is a numerical value corresponding to an increase in gainto the beneficiary (such as a purchase rate of an advertised commercialproduct or the like), the gain being expected to be larger in a casewhere the intervention is implemented to this certain one of the targetsthan in a case where the intervention is not implemented thereto.

For example, on the basis of target attribute information 311 (FIG. 5 )about predetermined attributes (such as sexes and ages) of each of theplurality of targets, the estimation-model generation unit 42 generatesthe estimation model for the predicted intervention effects thatrepresent predicted effects of the interventions if the interventionsare implemented. Specifically, on the basis of intervened-group resultinformation 341 (FIG. 4B), non-intervened-group result information 342(FIG. 4C), and the target attribute information 311 (FIG. 5 ), theestimation-model generation unit 42 generates an estimation model forestimating the predicted intervention effects from the predeterminedattributes of the targets (below, sometimes referred to as “targetattributes”).

The intervened-group result information 341 (FIG. 4B) containsinformation items about respective results of reactions by ones in anintervened group among the plurality of targets (such as whether or nothe/she has purchased the advertised commercial product or the like), theones having been subjected to the interventions, the intervened groupconsisting of these ones among the plurality of targets.

The non-intervened-group result information 342 (FIG. 4C) containsinformation items about respective results of reactions by other ones ina non-intervened group among the plurality of targets, the other oneshaving not been subjected to the interventions, the non-intervened groupconsisting of these other ones among the plurality of targets.

The target attribute information 311 (FIG. 5 ) contains informationitems about the respective attributes of the targets belonging to theintervened group or the non-intervened group.

A plurality of attribute items (such as sexes and ages) included as thepredetermined attributes of the targets are respectively indicated byfeatures. The estimation model to be generated by the estimation-modelgeneration unit 42 is, for example, a model that allows numerical valuesto be estimated as the predicted intervention effects, the numericalvalues each corresponding to a sum of products obtained by multiplyingthe plurality of features corresponding to the plurality of attributeitems respectively by weighting coefficients, that is, a linear model.Application of the respective target attributes of the targets to theestimation model, the attributes being included in the target attributeinformation 311 (FIG. 5 ), enables calculation of the respectivepredicted-intervention effects on the targets (FIG. 7 ).

The contribution-degree calculation unit 43 executes, on the basis ofthe estimation model obtained from the estimation-model generation unit42, a process of calculating contribution degrees that indicaterespective degrees by which the plurality of attribute items (such assexes and ages) included as the predetermined attributes of the targetscontribute to the predicted intervention effects. For example, when theestimation model is generated, the estimation model allowing thenumerical values to be estimated as the predicted intervention effects,the numerical values each corresponding to the sum of the productsobtained by multiplying the plurality of features corresponding to theplurality of attribute items respectively by the weighting coefficients,the contribution-degree calculation unit 43 calculates, among thecontribution degrees, a corresponding-one contribution degree of oneattribute item among the attribute items on the basis of, among theweighting coefficients, a corresponding-one weighting coefficient bywhich a corresponding one feature among the plurality of features ismultiplied, the corresponding one feature corresponding to the oneattribute item.

The screen generation unit 44 executes a process of generating displayscreens to be displayed on a display of the information processingapparatus 2 that accesses the server apparatus 1. The screen generationunit 44 generates the display screens so that, when the operations bythe user (content creator) are input to the information processingapparatus 2, information is provided in accordance with these operationsin a manner that the display screens are updated in response to theseoperations.

The screen generation unit 44 displays, on the display screen, at leastsome of the contribution degrees calculated respectively with regard tothe plurality of attribute items included as the predeterminedattributes of the targets. This enables the content creator to advancethe work of creating the contents while checking what kind of theattribute items contributes to the predicted intervention effects. Withthis, contents having greater intervention effects are easily created.

FIG. 3A is a table showing an example of the target information 31 to bestored in the storage unit 30. In the target information 31 shown inFIG. 3A, target IDs for identifying the targets from each other and thetarget attributes are associated with each other. The attribute itemssuch as sexes, ages, regions where the targets live, occupations, andthe number of times of visits to a particular WEB site are included asthe target attributes exemplified in FIG. 3A. The target information 31may further contain, as the information items to be associated with thetarget IDs, information items other than the target attributes (forexample, management information items such as dates when the informationitems are registered and expiration dates). The target attributeinformation 311 shown in FIG. 5 is an extract of ones of the targetattributes from the target information 31, the ones being necessary forgenerating the estimation model for the predicted intervention effects.

FIG. 3B is a table showing an example of the intervention information 32to be stored in the storage unit 30. In the intervention information 32shown in FIG. 3B, intervention IDs for identifying implementedinterventions from each other and predetermined attributes of theinterventions (below, sometimes referred to as “interventionattributes”) are associated with each other. Attribute items such ascategories of commercial products to be advertised by interventions,content IDs indicating contents used in the interventions, and thenumbers of times of repeating the interventions are included as theintervention attributes exemplified in FIG. 3B. The interventioninformation 32 may further contain, as the information items to beassociated with the intervention IDs, information items other than theintervention attributes (for example, the management information items).

FIG. 3C is a table showing an example of the content information 33 tobe stored in the storage unit 30. In the content information 33 shown inFIG. 3C, the content IDs for identifying the contents to be used in theinterventions from each other and predetermined attributes of thecontents (below, sometimes referred to as “content attributes”) areassociated with each other. Attribute items such as the numbers ofcharacters to be contained in the contents, whether or not humans aredepicted, whether or not animals are depicted, and background colors ofthe contents are included as the content attributes exemplified in FIG.3C. The content information 33 may further contain, as the informationitems to be associated with the content IDs, information items otherthan the content attributes (for example, the management informationitems).

Now, the operations in the server apparatus 1 according to thisembodiment, the server apparatus 1 having the above-describedconfiguration, are described with reference to a flowchart of FIG. 10 .

ST105:

The intervention implementation unit 41 implements the interventions forobtaining the information (result information 34 shown in FIG. 4A) to beused at a time when the estimation model for the predicted interventioneffects is generated in Step ST120 described below. The interventionimplementation unit 41 implements the interventions with respect to agroup of the ones of the targets, the ones being selected as targets inthe intervened group, and meanwhile, does not implement theinterventions with respect to another group of the other ones of thetargets, the other ones being selected as targets in the non-intervenedgroup. To which of the intervened group and the non-intervened group thetargets belong may be selected at random, or may be selected accordingto some predetermined rules.

Note that, in a case where, for example, the advertisement distributionsare implemented as the interventions, when the intervention(advertisement distribution) is repeatedly implemented to the sametarget, an effect of this intervention may decrease, or may even benegative. As a countermeasure, the number of the targets to be selectedas those in the intervened group from the target information 31 may beset to the number that satisfies a predetermined proportion to all theselectable targets in the target information 31, or to the number thatis fixed and is necessary and sufficient for generating the estimationmodel for the predicted intervention effects.

ST110:

The intervention implementation unit 41 records the result information34 (FIG. 4A) to the storage unit 30, the result information 34containing, respectively with regard to the targets belonging to theintervened group or the non-intervened group, whether or not theinterventions have been implemented, contents of the implementedinterventions, the results of the reactions by the targets, and thelike. Note that, if the results of the reactions by the targets areresults of whether or not he/she has purchased a commercial product,binary data (1 or 0) may be recorded as the results of the reactions.Alternatively, the results of the reactions by the targets may becontinuous values such as purchase prices of commercial products.

ST115:

The estimation-model generation unit 42 extracts information items aboutthe target attributes of the targets from the target information 31(FIG. 3A), the information items being used in generating the estimationmodel for the predicted intervention effects. Then, the estimation-modelgeneration unit 42 stores the extracted information items as the targetattribute information 311 (FIG. 5 ) to the storage unit 30. In thisexample, the targets IDs in the target attribute information 311 (FIG. 5) are systematized in the same way as the target IDs in the resultinformation 34 (FIG. 4A) are systematized. Note that, when it may beassumed that, among the targets, ones having target attributes similarto each other take similar actions in response to the interventions, thetarget IDs in the target attribute information 311 and the target IDs inthe result information 34 may be systematized in different ways, thatis, need not necessarily be directly linked to each other.

ST120:

By using the intervened-group result information 341 (FIG. 4B) and thenon-intervened-group result information 342 (FIG. 4C) that are containedin the result information 34 (FIG. 4A) obtained by the interventions inStep ST100, and by using the target attribute information 311 extractedin Step ST115, the estimation-model generation unit 42 generates theestimation model to be used for estimating the respectivepredicted-intervention effects on the targets. The estimation-modelgeneration unit 42 generates, by machine learning, estimation models fortargets for whom information items in both the result information 34obtained as a result of the interventions and the target attributeinformation 311 have been prepared, the estimation models being modelsfor estimating the predicted intervention effects from the targetattributes.

For example, the estimation-model generation unit 42 generates afirst-gain estimation model μ₁ for estimating gain to be obtained whenthe interventions are implemented (such as purchase rates of commercialproducts) from the target attributes on the basis of theintervened-group result information 341 (FIG. 4B) and of the targetattribute information 311 (FIG. 5 ). In addition, the estimation-modelgeneration unit 42 generates a second-gain estimation model μ₀ forestimating gain to be obtained when the interventions are notimplemented from the target attributes on the basis of thenon-intervened-group result information 342 (FIG. 4C) and of the targetattribute information 311 (FIG. 5 ). The first-gain estimation model piand the second-gain estimation model μ₀ can be generated by learningwith use of existing algorithms of regression and classification (suchas logistic regression).

FIG. 6A is a table showing the intervened-group result information 341and the target attribute information 311 to be used in generating thefirst-gain estimation model μ₁, and FIG. 6B is a table showing thenon-intervened-group result information 342 and the target attributeinformation 311 to be used in generating the second-gain estimationmodel μ₀. When the features indicating the target attributes are “X,”and when the gain (such as the purchase rates of the commercialproducts) is “Y,” the gain Y that is estimated by the first-gainestimation model μ₁ is expressed by “μ₁(X),” and the gain Y that isestimated by the second-gain estimation model μ₀ is expressed by“μ₀(X).”

Among the predicted intervention effects, a corresponding-one predictedintervention effect on one target among the targets can be calculated asa difference obtained by subtracting the gain estimated by applying,among the target attributes, a corresponding-one target attribute of theone target to the second-gain estimation model μ₀ from the gainestimated by applying the corresponding-one target attribute of the onetarget to the first-gain estimation model μ₁. For example, when, amongthe features, a feature indicating the corresponding-one targetattribute of the one target is “X_(new),” the estimation-modelgeneration unit 42 calculates a predicted intervention effect τ(X_(new))of the one target by the following equation.

[Math 1]

τ(X _(new))=μ₁(X _(new))−μ₀(X _(new))   (1)

When the gain is the purchase rate of the commercial product or thelike, the predicted intervention effect τ(X_(new)) expressed by Equation(1) corresponds to a result of subtraction of a predicted purchase rate(μ₀(X_(new))) at the time when the intervention is not implemented froma predicted purchase rate (μ₁(X_(new)) at the time when the interventionis implemented. FIG. 7 is a table showing an example of the predictedintervention effects calculated respectively with regard to the targetsin the target information 31.

When both the first-gain estimation model μ₁ and the second-gainestimation model μ₀ are the linear models, an estimation model for thepredicted intervention effects, the estimation model being expressed byEquation (1), is also the linear model. In other words, by theestimation model that is expressed by Equation (1), the numerical valueseach corresponding to the sum of the products obtained by multiplyingthe plurality of features corresponding to the plurality of attributeitems respectively by the weighting coefficients are each estimated asthe predicted intervention effect. FIG. 8 is a table showing an exampleof the respective weighting coefficients of the attribute items in thelinear estimation model. In the example shown in FIG. 8 , the sexes andthe ages are used as discrete features, and the number of times ofvisits to a WEB site is used as a continuous-value feature. All thefeatures are appropriately normalized such that, for example, an averageis zero and a variance is one. Whether weights in this case are positiveor negative corresponds to whether the intervention effects are positiveor negative, and an increase in absolute value of the weights representsan increase in contribution degree to the intervention effects.

ST125:

The contribution-degree calculation unit 43 calculates, on the basis ofthe estimation model acquired in Step ST120, the contribution degreesthat indicate the respective degrees by which the plurality of attributeitems (such as sexes and ages) included as the predetermined attributesof the targets contribute to the predicted intervention effects. Forexample, when the linear estimation model as expressed by Equation (1)is acquired, the contribution-degree calculation unit 43 calculates,among the contribution degrees, a corresponding-one contribution degreeof one attribute item among the attribute items on the basis of, amongthe weighting coefficients, a corresponding-one weighting coefficient bywhich a corresponding one feature among the plurality of features ismultiplied, the corresponding one feature corresponding to the oneattribute item. When the features are appropriately normalized, thecontribution-degree calculation unit 43 may, for example, acquire theweighting coefficients respectively as the contribution degrees of theattribute items. Plus signs and minus signs of the weightingcoefficients in this case correspond to plus signs and minus signs ofthe intervention effects, and an increase in absolute value of theweighting coefficient represents an increase in contribution degree tothe intervention effects.

Alternatively, the contribution-degree calculation unit 43 may calculatethe respective contribution degrees of the attribute items with use of amodel that can be understood by humans, such as a decision tree, themodel being generated by relearning with use of the predictedintervention effects to be obtained from the estimation model acquiredin Step ST120. In other words, a technique for enabling description ofthe calculation of the contribution degrees with regard to the featureswithout dependence on the original estimation model acquired in StepST120 may be adopted.

For example, the contribution-degree calculation unit 43 calculates therespective predicted-intervention effects on the plurality of targets onthe basis of the target attribute information 311 (FIG. 5 ) about thepredetermined attributes of each of the plurality of targets, and on thebasis of the estimation model acquired in Step ST120. Thecontribution-degree calculation unit 43 classifies the plurality oftargets according to the plurality of attribute items with the predictedintervention effects being regarded as response variables, theclassifying being performed with use of the model such as the decisiontree (regression tree), the model being generated by learning on thebasis of the predicted intervention effects calculated respectively withregard to the plurality of targets, and on the basis of the targetattribute information 311 (FIG. 5 ). Then, the contribution-degreecalculation unit 43 calculates the contribution degrees of the pluralityof attribute items each on the basis of an average of thepredicted-intervention effects on all the plurality of targets, each onthe basis of a corresponding one of respective averages of thepredicted-intervention effects on a group of targets classifiedaccording to the plurality of attribute items, and each on the basis ofa corresponding one of the headcounts of the group of targets classifiedaccording to the plurality of attribute items.

FIG. 9 is a chart showing an example of results obtained by analyzing atendency of the predicted intervention effects corresponding to theattribute items of the targets with use of the decision tree (regressiontree). The response variables in learning for generating this estimationmodel are transformed outcomes. How much the features contribute towhich of positives and negatives of the predicted intervention effectscan be grasped, for example, by evaluating how much higher (or lower)each of the average values of the predicted intervention effects on onesof the targets, the ones corresponding to a certain one of the attributeitems, are than the average value of the predicted intervention effectson all the targets, the evaluating being performed with regard to eachof the attribute items that are split in the decision tree and beingperformed in consideration of the number of the ones of the targets.Thus, the respective contribution degrees of the attribute items caneach be calculated, for example, as a product obtained by multiplying,by the number of the ones of the targets, the ones corresponding to acertain one of the attribute items, a difference between a correspondingone of the average values of the predicted intervention effects on theones of the targets and the average value of the predicted interventioneffects on all the targets.

In the example shown in FIG. 9 , the respective contribution degrees ofthe attribute items, the degrees being classified with use of thedecision tree, can be calculated as follows.

Sex—Male: 15×(2.0−1.5)=+7.5

Sex—Female (≠Male): 35×(1.3−1.5)=−7.0

Sex—Female at or over age of 30: 20×(1.5−1.5)=0

Sex—Female under age of 30: 15×(0.7−1.5)=−12.0

What whether the contribution degrees calculated in such a way arepositive or negative and their absolute values represent is the same aswhat whether the weighting coefficients of the linear model describedabove are positive or negative and their absolute values represent.

ST130:

The processing unit 40 executes a process of determining the number ofthe contents to be created in accordance with reward that is set by thebeneficiary. This reward is presented by the beneficiary for the work ofcreating the contents. The content creator creates the contents as manyas the number of the contents to be created, the number having beendetermined in accordance with the reward.

ST135:

The screen generation unit 44 executes the process of generating thedisplay screens that can be displayed on the display of the informationprocessing apparatus 2 to be used in the work of creating the contents.FIG. 11 is a view showing an example of a display screen 50 to begenerated by the screen generation unit 44. In a central portion of thedisplay screen 50 shown in FIG. 11 , a window 51 that defines an area inwhich the contents such as banners are created is provided. In thewindow 51, content materials (such as photographs, illustrations,graphics, and a window in which characters of an advertising slogan aredisplayed) are arranged. The user (content creator) arbitrarily selectsthe content materials by operating icons with a mouse or the like. Withthis, the content materials can be arranged at arbitrary positions inthe window 51.

The screen generation unit 44 displays, on the display screen, at leastsome of the contribution degrees calculated respectively with regard tothe plurality of attribute items. For example, in an upper left area ofthe display screen 50 shown in FIG. 11 , a list 52 that is entitled“CONTRIBUTION DEGREE OF ATTRIBUTE ITEM” and shows respectivecontribution degrees of the attribute items is displayed. With this, theuser (content creator) can easily create contents having greaterintervention effects while checking the contribution degreescorresponding to the attribute items.

In addition, the content materials (materials of the contents) to bedisplayed on the display screen by the screen generation unit 44 may bedisplayed in a manner of corresponding to the contribution degrees ofthe attribute items (such as sexes and ages). For example, the storageunit 30 stores content materials (such as a photograph of a person forwomen and an advertising slogan for young people) associated with theattribute items (such as sexes and ages) of the targets. The screengeneration unit 44 selects one or more content materials among theplurality of content materials each associated with at least oneattribute item among the plurality of attribute items, the one or morecontent materials being associated with, among the plurality ofattribute items, attribute items having relatively-high contributiondegrees among the contribution degrees. The screen generation unit 44displays, on the display screen, the selected one or more contentmaterials as a candidate for the content materials that can be used increating the contents.

For example, near an end portion on the right of the display screen 50shown in FIG. 11 , a content-material arrangement field 53 for enablingthe user to arbitrarily select the content materials is provided. Thecontent-material arrangement field 53 includes a plurality of fields 62that can be collapsed by operations to their respective tabs 61. Theplurality of content materials associated with a common one of theattribute items are arranged in each of the fields 62. In a stateexemplified in FIG. 11 , among the fields 62, a field 62 in whichcontent materials that are for 30s having highest contribution degreesare arranged is developed. With this, content materials associated withattribute items having relatively-high contribution degrees are easilyused in creating the contents.

In addition, in the content-material arrangement field 53 on the displayscreen 50 shown in FIG. 11 , the plurality of fields 62 are arranged ina manner that, as contribution degrees of attribute items to which onesof the content materials correspond become higher, among the fields 62,fields 62 including these ones of the content materials are locatedhigher. In other words, in displaying the plurality of content materialsassociated with the attribute items having relatively-high contributiondegrees on the display screen 50, the screen generation unit 44 arrangesthe plurality of content materials in an order corresponding to thecontribution degrees of the attribute items with which the contentmaterials are associated. With this, contribution degrees can be easilygrasped from the order of the arrangement of the content materials.

Further, the screen generation unit 44 may display, on the displayscreen 50, contents used in interventions that have causedrelatively-great intervention effects in previous interventions (forexample, immediately preceding interventions). For example, the screengeneration unit 44 selects one or more contents among a plurality ofcontents that are used in the previous interventions, the one or morecontents corresponding to relatively-high averages among averages of thepredicted interventions effects on all targets to which theinterventions have been implemented, and displays the selected one ormore contents as reference information on the display screen 50.

For example, near a center on the left of the display screen 50 shown inFIG. 11 , a field 54 in which the contents (such as banners) that areused in the previous interventions are arranged is provided. In thisfield 54, upper two of the contents indicate two contents thatcorrespond to the relatively-high averages among the averages of thepredicted interventions effects. With this, contents can be created withreference to contents that have caused great intervention effects amongcontents used in previous interventions. Thus, contents having greaterintervention effects are easily created.

Still further, the screen generation unit 44 may display, on the displayscreen 50, contents that have caused relatively-great interventioneffects on users with attributes having high contribution degrees amongthe contents used in the previous interventions. For example, the screengeneration unit 44 acquires, with regard to the plurality of contentsused in the previous interventions, respective relatively-high averagesamong the averages of the predicted intervention effects on some targetsamong all targets to which the interventions have been implemented (sometargets corresponding to one or more attribute items among the pluralityof attribute items, the one or more attribute items havingrelatively-high contribution degrees). The screen generation unit 44selects, from the plurality of contents used in the previousinterventions, one or more contents corresponding to theserelatively-high averages among the averages of the predictedintervention effects. The screen generation unit 44 displays thisselected one or more contents as the reference information on thedisplay screen 50.

For example, in the field 54 on the display screen 50 shown in FIG. 11 ,lower two of the contents (banners) indicate two contents used ininterventions that correspond to relatively-high averages among averagesof the predicted intervention effects on targets in their “30s” who havethe highest contribution degrees. With this, contents can be createdwith reference to contents that have caused great intervention effectson targets with attributes having high contribution degrees amongcontents used in previous interventions. Thus, contents having greateffects exclusively on the targets with the attributes having highcontribution degrees are easily created.

Yet further, the screen generation unit 44 may calculate respectivesimilarities between a plurality of previously created contents andcurrently created contents on the display screen 50, select, from theplurality of created contents, one or more created contents havingrelatively high similarities among the respective similarities, anddisplay the selected one or more created contents as the referenceinformation on the display screen 50. The similarities between thecontents may be calculated, for example, from similarities betweenimages of the contents, similarities between combinations of contentmaterials used in the contents, or from overlapping degrees of characterstrings in the contents.

For example, on a lower left side of the display screen 50 shown in FIG.11 , a field 55 in which three contents (banners) that haverelatively-high similarities to the currently created contents isprovided. By referring to the previously created contents that aresimilar to the currently created contents in this way, an efficiency increating contents is easily increased. In addition, commonalities to anddifferences from the previously created contents can be checked, andhence various and diverse contents are easily created.

Yet further, the screen generation unit 44 may display, on the displayscreen 50, information about the number of the contents to be created,the number being determined in Step ST130. On the display screen 50shown in FIG. 11 , a field 56 in which the number of the contents to becreated is displayed is provided at a lower right corner. With this,work can be efficiently advanced while the number of the contents to becreated is checked.

ST140:

The processing unit 40 stores the contents created on the display screen50 generated by the screen generation unit 44 as the created contentsinto the storage unit 30. For example, the processing unit 40 registersthe information items about the contents as shown in FIG. 3C with thecontent information 33.

As described hereinabove, according to this embodiment, the contributiondegrees that indicate respective degrees by which the plurality ofattribute items included as the predetermined attributes of the targetscontribute to the predicted intervention effects are calculated on thebasis of the estimation model for estimating the predicted interventioneffects from the predetermined attributes. Then, at least some of thecontribution degrees calculated respectively with regard to theplurality of attribute items are displayed on the display screen forwork of creating contents. With this, the work of creating contents canbe advanced while what kind of the attribute items contributes to thepredicted intervention effects is checked. Thus, contents having greaterintervention effects are easily created.

Note that, the present invention is not limited only to theabove-described embodiment, and may be embodied in various other formsthat persons skilled in the art could easily conceive.

The display screen 50 for work of creating contents, the screen beingshown in FIG. 11 , is merely an example, and the present invention isnot limited to this example. Specifically, the arrangement and visualforms such as sizes of the fields on the display screen 50 are merelyexamples, and may be arbitrarily changed. In addition, the contents tobe created while being assisted are not limited to the banners foradvertising, and may be, for example, contents including moving imagesand voice.

1. An apparatus configured to assist in creation of contents to be usedin interventions, the apparatus comprising: a processing unit includingat least one processor; and a storage unit configured to store a commandto be executed by the processing unit, wherein the interventions includepresentation of the contents to a plurality of targets so that theplurality of targets provoke reactions that a beneficiary wants,wherein, among respective predicted-intervention effects on theplurality of targets, one predicted-intervention effect on one targetamong the plurality of targets is a numerical value corresponding to anincrease in gain to the beneficiary, the gain being larger in a casewhere a corresponding single intervention among the interventions isimplemented to the one target than in a case where the correspondingsingle intervention is unimplemented to the one target, wherein the gainis a numerical value that is set in accordance with a result of acorresponding one reaction by the one target among the reactions,wherein the processing unit executes, in accordance with the command, aprocess of calculating contribution degrees that indicate respectivedegrees by which a plurality of attribute items included aspredetermined attributes of the one target contribute to the onepredicted-intervention effect, the calculating being performed on abasis of an estimation model for estimating the onepredicted-intervention effect from the predetermined attributes of theone target, and a process of generating a display screen that can bedisplayed on a display to be used in work of creating the contents,wherein the process of generating the display screen includes generatingthe display screen on which at least one contribution degree among thecontribution degrees calculated respectively with regard to theplurality of attribute items is displayed.
 2. The apparatus according toclaim 1, wherein the plurality of attribute items is respectivelyindicated by a plurality of features, wherein the estimation model is amodel that estimates numerical value as the one predicted-interventioneffect, the numerical value corresponding to a sum of products obtainedby multiplying the plurality of features corresponding to the pluralityof attribute items respectively by weighting coefficients, and whereinthe process of calculating the contribution degrees includescalculating, among the contribution degrees, a corresponding-onecontribution degree of one attribute item among the plurality ofattribute items on a basis of, among the weighting coefficients, acorresponding-one weighting coefficient by which a corresponding onefeature among the plurality of features is multiplied, the correspondingone feature corresponding to the one attribute item.
 3. The apparatusaccording to claim 1, wherein the process of calculating thecontribution degrees includes calculating the respectivepredicted-intervention effects on the plurality of targets on a basis oftarget attribute information about the predetermined attributes of eachof the plurality of targets, and on the basis of the estimation model,classifying the plurality of targets according to the plurality ofattribute items with the respective predicted-intervention effects beingregarded as response variables, the classifying being performed on abasis of the respective predicted-intervention effects calculated withregard to the plurality of targets, and on the basis of the targetattribute information, and calculating the contribution degrees of theplurality of attribute items, each on a basis of an average of therespective predicted-intervention effects on all the plurality oftargets, each on a basis of a corresponding one of respective averagesof the respective predicted-intervention effects on a group of targetsclassified according to the plurality of attribute items, and each on abasis of a corresponding one of the headcounts of the group of targetsclassified according to the plurality of attribute items.
 4. Theapparatus according to claim 1, wherein the contents are made of aplurality of content materials each associated with at least oneattribute item among the plurality of attribute items, and wherein theprocess of generating the display screen includes selecting at least onecontent material among the plurality of content materials, the at leastone content material being associated with, among the plurality ofattribute items, one attribute item having a relatively-highcontribution degree among the contribution degrees, and generating thedisplay screen on which the selected at least one content material isdisplayed as a candidate for the plurality of content materials that canbe used in creating the contents.
 5. The apparatus according to claim 4,wherein the process of generating the display screen includes selectingthe plurality of content materials, and generating the display screen onwhich the selected plurality of content materials is arranged in anorder corresponding to the contribution degrees of the plurality ofattribute items with which the plurality of content materials areassociated.
 6. The apparatus according to claim 1, wherein theintervention includes previous interventions, wherein the contentsinclude a plurality of contents used in the previous interventions,wherein the respective predicted-intervention effects on the pluralityof targets include other respective predicted-intervention effects onall the plurality of targets to which the interventions have beenimplemented, and a still other predicted-intervention effect on at leastone target among all the plurality of targets to which the interventionshave been implemented, the at least one target corresponding to at leastone attribute item among the plurality of attribute items, the at leastone attribute item having a high contribution degree among thecontribution degrees, wherein the process of generating the displayscreen includes at least one of selecting, from the plurality ofcontents used in the previous interventions, at least one contentcorresponding to a high average among averages of the other respectivepredicted-intervention effects, or selecting, from the plurality ofcontents used in the previous interventions, at least one contentcorresponding to a high average among averages of the still otherpredicted-intervention effect, and wherein the process of generating thedisplay screen includes generating the display screen on which theselected at least one content is displayed as reference information. 7.The apparatus according to claim 1, wherein the contents include aplurality of previously created contents, and wherein the process ofgenerating the display screen includes calculating similarities betweenthe plurality of previously created contents and currently createdcontents on the display screen, selecting, from the plurality ofpreviously created contents, at least one previously-created contenthaving a relatively high similarity among the similarities, andgenerating the display screen on which the selected at least onepreviously-created content is displayed as reference information.
 8. Theapparatus according to claim 1, wherein the processing unit executes aprocess of determining the number of the contents to be created, theprocess being executed in accordance with the command, the number beingdetermined in accordance with reward that is set by the beneficiary, andwherein the process of generating the display screen includes generatingthe display screen on which information about the determined number ofthe contents to be created is displayed.
 9. A method of assistingcreation of contents to be used in interventions, the interventionsincluding presentation of the contents to a plurality of targets so thatthe plurality of targets provoke reactions that a beneficiary wants,among respective predicted-intervention effects on the plurality oftargets, one predicted-intervention effect on one target among theplurality of targets being a numerical value corresponding to anincrease in gain to the beneficiary, the gain being expected to belarger in a case where a corresponding single intervention among theinterventions is implemented to the one target than in a case where thecorresponding single intervention is unimplemented to the one target,the gain being a numerical value that is set in accordance with a resultof a corresponding one reaction by the one target among the reactions,the method comprising: calculating, by at least one computer,contribution degrees that indicate respective degrees by which aplurality of attribute items included as predetermined attributes of theone target contribute to the one predicted-intervention effect, thecalculating being performed on a basis of an estimation model forestimating the one predicted-intervention effect from the predeterminedattributes of the one target; and generating, by the at least onecomputer, a display screen that can be displayed on a display to be usedin work of creating the contents, wherein the generating of the displayscreen by the at least one computer includes generating the displayscreen on which at least one contribution degree among the contributiondegrees calculated respectively with regard to the plurality ofattribute items is displayed.
 10. A computer-readable recording mediumthat records a program which assists creation of contents to be used ininterventions, the interventions including presentation of the contentsto a plurality of targets so that the plurality of targets provokereactions that a beneficiary wants, among respectivepredicted-intervention effects on the plurality of targets, onepredicted-intervention effect on one target among the plurality oftargets being a numerical value corresponding to an increase in gain tothe beneficiary, the gain being expected to be larger in a case where acorresponding single intervention among the interventions is implementedto the one target than in a case where the corresponding singleintervention is unimplemented to the one target, the gain being anumerical value that is set in accordance with a result of acorresponding one reaction by the one target among the reactions, theprogram causing at least one computer to execute: a process ofcalculating contribution degrees that indicate respective degrees bywhich a plurality of attribute items included as predeterminedattributes of the one target contribute to the onepredicted-intervention effect, the calculating being performed on abasis of an estimation model for estimating the onepredicted-intervention effect from the predetermined attributes of theone target; and a process of generating a display screen that can bedisplayed on a display to be used in work of creating the contents,wherein the process of generating the display screen includes generatingthe display screen on which at least one contribution degree among thecontribution degrees calculated respectively with regard to theplurality of attribute items is displayed.